基于视觉的AUV自主水下管线跟踪方法

Vision-based AUV Autonomous Underwater Pipeline Tracking Method

对海下管线的检测是保证其正常运行的必要环节。文章针对海下管道巡检问题,提出一种结合状态表示学习和深度强化学习的方法,使水下机器人能够基于图像进行管线跟踪。利用无监督表征学习方法提取海底管线图像特征,结合任务情况设计了动作、状态空间和奖励函数,通过SAC算法学习一个稳健的跟踪控制策略。最后搭建仿真环境进行试验,证明了所提出方法的有效性和泛化性。

The detection of subsea pipelines is a necessary link to ensure its normal operation.For the subsea pipeline inspection problem,this paper proposes a method combining state representation learning and deep reinforcement learning,so that the underwater robot can track the pipeline based on image.The unsupervised representation learning method is used to extract the image features of submarine pipelines.Combined with the task situation,the action,state space and reward function are designed.A robust tracking control strategy is learned through SAC algorithm.Finally,a simulation environment is built to test the effectiveness and generalization of the proposed method.